{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "%matplotlib inline\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "white_data = pd.DataFrame()\n",
    "black_data = pd.DataFrame()\n",
    "\n",
    "def yield_orgin_csv(pd_name, file_type):\n",
    "    all_api_names, all_call_pids, all_ret_values, all_exinfos = [], [], [], []\n",
    "    flag = 1\n",
    "    for path in glob.glob(\"./stage1_dataset/train/{}/*\".format(file_type)):\n",
    "        with open(path, \"r\") as fp:\n",
    "            xml = BeautifulSoup(fp, \"lxml\")\n",
    "        action = xml.find_all(\"action\")\n",
    "        api_names, call_pids, ret_values, exinfos = [], [], [], []\n",
    "        for a in action:\n",
    "            try:\n",
    "                api_names.append(a.get(\"api_name\"))\n",
    "            except:\n",
    "                api_names.append(\"\")\n",
    "            try:\n",
    "                call_pids.append(a.get(\"call_pid\"))\n",
    "            except:\n",
    "                call_pids.append(\"\")\n",
    "            try:\n",
    "                ret_values.append(a.get(\"ret_value\"))\n",
    "            except:\n",
    "                ret_values.append(\"\")\n",
    "            try:\n",
    "                for exinfo in a.find_all(\"exinfo\"):\n",
    "                    exinfos.append(exinfo.get(\"value\"))\n",
    "            except:\n",
    "                exinfos.append(\"\")\n",
    "        try:\n",
    "            all_api_names.append(\" \".join(api_names))\n",
    "        except:\n",
    "            all_api_names.append(\"\")\n",
    "        try:\n",
    "            all_call_pids.append(\" \".join(call_pids))\n",
    "        except:\n",
    "            all_call_pids.append(\"\")\n",
    "        try:\n",
    "            all_ret_values.append(\" \".join(ret_values))\n",
    "        except:\n",
    "            all_ret_values.append(\"\")\n",
    "        try:\n",
    "            all_exinfos.append(\" \".join(exinfos))\n",
    "        except:\n",
    "            all_exinfos.append(\"\")\n",
    "        \n",
    "        \n",
    "        if flag % 300 == 0:\n",
    "            print(flag)\n",
    "        flag += 1\n",
    "        \n",
    "    pd_name[\"api_name\"] = all_api_names\n",
    "    pd_name[\"call_pid\"] = all_call_pids\n",
    "    pd_name[\"ret_value\"] = all_ret_values\n",
    "    pd_name[\"exinfos\"] = all_exinfos\n",
    "    pd_name.to_csv(\"{}_data.csv\".format(file_type), index=False, encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "yield_orgin_csv(white_data, \"white\")\n",
    "yield_orgin_csv(white_data, \"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拼接black_data和white_data\n",
    "black_data = pd.read_csv(\"black_data.csv\", encoding=\"iso8859-1\")\n",
    "white_data = pd.read_csv(\"white_data.csv\", encoding=\"utf-8\")\n",
    "origin_train_data = pd.concat([black_data, white_data])\n",
    "safe_type = [1 for _ in range(black_data.shape[0])] + [0 for _ in range(white_data.shape[0])]\n",
    "origin_train_data[\"safe_type\"] = safe_type\n",
    "origin_train = origin_train_data.dropna(how=\"any\")\n",
    "origin_train.to_csv(\"origin_train_data.csv\", encoding=\"utf-8\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"origin_train_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>api_name</th>\n",
       "      <th>call_pid</th>\n",
       "      <th>ret_value</th>\n",
       "      <th>exinfos</th>\n",
       "      <th>safe_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>SyStem.exe 212 \"C:\\program\\1.exe\"  direct C:\\p...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            api_name  \\\n",
       "0  AnalyzeStart Fake_BeCreatedEx TryToAnalyze Loa...   \n",
       "1  AnalyzeStart Fake_BeCreatedEx TryToAnalyze Loa...   \n",
       "2  AnalyzeStart Fake_BeCreatedEx TryToAnalyze Loa...   \n",
       "3  AnalyzeStart Fake_BeCreatedEx TryToAnalyze Loa...   \n",
       "4  AnalyzeStart Fake_BeCreatedEx TryToAnalyze Loa...   \n",
       "\n",
       "                                            call_pid  \\\n",
       "0  0 396 396 396 396 396 396 396 396 396 396 396 ...   \n",
       "1  0 396 396 396 396 396 396 396 396 396 396 396 ...   \n",
       "2  0 396 396 396 396 396 396 396 396 396 396 396 ...   \n",
       "3  0 396 396 396 396 396 396 396 396 396 396 396 ...   \n",
       "4  0 396 396 396 396 396 396 396 396 396 396 396 ...   \n",
       "\n",
       "                                           ret_value  \\\n",
       "0  0 0 0 0 0 0 0 0 0 0 0 c0000034 0 0 0 c0000034 ...   \n",
       "1  0 0 0 0 0 0 0 0 0 c0000034 0 c0000034 0 c00000...   \n",
       "2  0 0 0 0 0 0 0 0 0 0 0 0 0 c0000034 0 c0000034 ...   \n",
       "3  0 0 0 0 0 0 0 0 0 0 0 c0000034 0 0 0 0 c000003...   \n",
       "4  0 0 0 0 0 0 0 0 0 0 0 0 c0000034 0 c0000034 0 ...   \n",
       "\n",
       "                                             exinfos  safe_type  \n",
       "0  SyStem.exe 212 \"C:\\program\\1.exe\"  direct C:\\p...          0  \n",
       "1  SyStem.exe 212 \"C:\\program\\1.exe\"  direct C:\\p...          0  \n",
       "2  SyStem.exe 212 \"C:\\program\\1.exe\"  direct C:\\p...          0  \n",
       "3  SyStem.exe 212 \"C:\\program\\1.exe\"  direct C:\\p...          0  \n",
       "4  SyStem.exe 212 \"C:\\program\\1.exe\"  direct C:\\p...          0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 29979 entries, 0 to 29978\n",
      "Data columns (total 5 columns):\n",
      "api_name     29979 non-null object\n",
      "call_pid     29979 non-null object\n",
      "ret_value    29979 non-null object\n",
      "exinfos      29979 non-null object\n",
      "safe_type    29979 non-null int64\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_index_string(keys_set, length=12):\n",
    "    keys = []\n",
    "    for key in keys_set:\n",
    "        if len(key) > length:\n",
    "            keys.append(key[:12])\n",
    "        else:\n",
    "            keys.append(key)\n",
    "    return keys\n",
    "    \n",
    "def plot_and_compute_top_features(origin_train_data, column, plot=False):\n",
    "    black_api_names = origin_train_data[origin_train_data[\"safe_type\"] == 1][\"{}\".format(column)]\n",
    "    white_api_names = origin_train_data[origin_train_data[\"safe_type\"] == 0][\"{}\".format(column)]\n",
    "    black_api_name_list = [name for black in black_api_names for name in black.split()]\n",
    "    white_api_name_list = [name for white in white_api_names for name in white.split()] \n",
    "    black_counter = Counter(black_api_name_list)\n",
    "    white_counter = Counter(white_api_name_list)\n",
    "    black_api_name_pd = pd.DataFrame()\n",
    "    black_api_name_pd[\"count\"] = black_counter.values()\n",
    "    black_api_name_pd.index = adjust_index_string(black_counter.keys())\n",
    "    \n",
    "    white_api_name_pd = pd.DataFrame()\n",
    "    white_api_name_pd[\"count\"] = white_counter.values()\n",
    "    white_api_name_pd.index = adjust_index_string(white_counter.keys())\n",
    "    if plot:\n",
    "        fig, axs = plt.subplots(1, 2, figsize=(25, 8))\n",
    "        black_api_name_pd.sort_values(by=\"count\", ascending=True)[-30:].plot(kind=\"barh\", ax=axs[0])\n",
    "        axs[0].set_title(\"black_sample\")\n",
    "        white_api_name_pd.sort_values(by=\"count\", ascending=True)[-30:].plot(kind=\"barh\", ax=axs[1])\n",
    "        axs[1].set_title(\"white_sample\")\n",
    "        plt.tight_layout()\n",
    "        fig.savefig(\"{}_barh.pdf\".format(column))\n",
    "    return black_api_name_pd.sort_values(by=\"count\", ascending=True).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# api_name_index = plot_and_compute_top_features(train, \"api_name\", plot=True)\n",
    "# call_pid_index = plot_and_compute_top_features(train, \"call_pid\", plot=True)\n",
    "# exinfos_index = plot_and_compute_top_features(train, \"exinfos\", plot=True)\n",
    "# ret_values_index = plot_and_compute_top_features(train, \"ret_value\", plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_diff_features(train_data, column, plot=False):\n",
    "    black_api_names = train_data[train_data[\"safe_type\"] == 1][\"{}\".format(column)]\n",
    "    white_api_names = train_data[train_data[\"safe_type\"] == 0][\"{}\".format(column)]\n",
    "    black_api_name_set = set([name for black in black_api_names for name in black.split()])\n",
    "    white_api_name_set = set([name for white in white_api_names for name in white.split()]) \n",
    "    return black_api_name_set.symmetric_difference(white_api_name_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# api_name_diff = compute_diff_features(origin_train, \"api_name\")\n",
    "# call_pid_diff = compute_diff_features(origin_train, \"call_pid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理test数据集\n",
    "def yield_orgin_test_csv(pd_name, file_type):\n",
    "    all_api_names, all_call_pids, all_ret_values, all_exinfos, id_ = [], [], [], [], []\n",
    "    flag = 1\n",
    "    for path in glob.glob(\"./stage1_dataset/{}/*\".format(file_type)):\n",
    "        with open(path, \"r\") as fp:\n",
    "            xml = BeautifulSoup(fp, \"lxml\")\n",
    "        action = xml.find_all(\"action\")\n",
    "        api_names, call_pids, ret_values, exinfos = [], [], [], []\n",
    "        for a in action:\n",
    "            try:\n",
    "                api_names.append(a.get(\"api_name\"))\n",
    "            except:\n",
    "                api_names.append(\"\")\n",
    "            try:\n",
    "                call_pids.append(a.get(\"call_pid\"))\n",
    "            except:\n",
    "                call_pids.append(\"\")\n",
    "            try:\n",
    "                ret_values.append(a.get(\"ret_value\"))\n",
    "            except:\n",
    "                ret_values.append(\"\")\n",
    "            try:\n",
    "                for exinfo in a.find_all(\"exinfo\"):\n",
    "                    exinfos.append(exinfo.get(\"value\"))\n",
    "            except:\n",
    "                exinfos.append(\"\")\n",
    "        try:\n",
    "            all_api_names.append(\" \".join(api_names))\n",
    "        except:\n",
    "            all_api_names.append(\"\")\n",
    "        try:\n",
    "            all_call_pids.append(\" \".join(call_pids))\n",
    "        except:\n",
    "            all_call_pids.append(\"\")\n",
    "        try:\n",
    "            all_ret_values.append(\" \".join(ret_values))\n",
    "        except:\n",
    "            all_ret_values.append(\"\")\n",
    "        try:\n",
    "            all_exinfos.append(\" \".join(exinfos))\n",
    "        except:\n",
    "            all_exinfos.append(\"\")\n",
    "        id_.append(path.split('.')[1].split('/')[-1]) \n",
    "        \n",
    "        \n",
    "        if flag % 300 == 0:\n",
    "            print(flag)\n",
    "        flag += 1\n",
    "        \n",
    "    pd_name[\"api_name\"] = all_api_names\n",
    "    pd_name[\"call_pid\"] = all_call_pids\n",
    "    pd_name[\"ret_value\"] = all_ret_values\n",
    "    pd_name[\"exinfos\"] = all_exinfos\n",
    "    pd_name[\"id\"] = id_\n",
    "    pd_name.to_csv(\"{}_data.csv\".format(file_type), index=False, encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pd = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "yield_orgin_test_csv(test_pd, \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 5 columns):\n",
      "api_name     15000 non-null object\n",
      "call_pid     15000 non-null object\n",
      "ret_value    15000 non-null object\n",
      "exinfos      15000 non-null object\n",
      "id           15000 non-null object\n",
      "dtypes: object(5)\n",
      "memory usage: 586.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_pd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_name_index = plot_and_compute_top_features(train_data, \"api_name\")\n",
    "call_pid_index = plot_and_compute_top_features(train_data, \"call_pid\")\n",
    "exinfos_index = plot_and_compute_top_features(train_data, \"exinfos\")\n",
    "ret_values_index = plot_and_compute_top_features(train_data, \"ret_value\")\n",
    "def yield_last_data_set(pd_name, \n",
    "                        api_name_index, \n",
    "                        call_pid_index, \n",
    "                        ret_values_index,\n",
    "                        exinfos_index, \n",
    "                        type_):\n",
    "    api_name_set = api_name_index[-80:]\n",
    "    call_pid_set = list(call_pid_index[-12:]) + [\"1396\", \"2692\", \"2672\", \"2696\"]\n",
    "    exinfos_set = exinfos_index[-20:]\n",
    "    ret_value_set = ret_values_index[-20:]\n",
    "    \n",
    "    for feature in api_name_set:\n",
    "        pd_name[feature] = pd_name[\"api_name\"].apply(lambda x: x.split().count(feature))\n",
    "    for feature in call_pid_set:\n",
    "        pd_name[feature] = pd_name[\"call_pid\"].apply(lambda x: x.split().count(feature))\n",
    "    for feature in exinfos_set:\n",
    "        pd_name[feature] = pd_name[\"exinfos\"].apply(lambda x: x.split().count(feature))\n",
    "    for feature in ret_value_set:\n",
    "        pd_name[feature] = pd_name[\"ret_value\"].apply(lambda x: x.split().count(feature))\n",
    "        \n",
    "    new = pd_name.drop([\"api_name\", \"call_pid\", \"ret_value\", \"exinfos\"], axis=1)\n",
    "    new_pd = new.sample(frac=1.0)\n",
    "    new_pd.to_csv(\"{}.csv\".format(type_), index=False, encoding=\"utf-8\")\n",
    "    return new_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv(\"origin_test_data.csv\")\n",
    "fliter_test_data_2 = yield_last_data_set(test_data, \n",
    "                                         api_name_index, \n",
    "                                         call_pid_index, \n",
    "                                         ret_values_index,\n",
    "                                         exinfos_index, \n",
    "                                         \"fliter_test_data_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "origin_train = pd.read_csv(\"origin_train_data.csv\")\n",
    "fliter_train_data_2 = yield_last_data_set(origin_train, \n",
    "                                          api_name_index, \n",
    "                                          call_pid_index,\n",
    "                                          ret_values_index,\n",
    "                                          exinfos_index,\n",
    "                                          \"fliter_train_data_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "safe_type = fliter_train_data_2[\"safe_type\"]\n",
    "features = fliter_train_data_2.iloc[:, 2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "train_data, test_data, train_label, test_label = train_test_split(features, \n",
    "                                                                  safe_type, \n",
    "                                                                  test_size=0.2, \n",
    "                                                                  random_state=0)\n",
    "def plot(test_label, y_pred, model):\n",
    "    font = {\"color\": \"darkred\",\n",
    "            \"size\": 13, \n",
    "            \"family\" : \"serif\"}\n",
    "\n",
    "    accs = accuracy_score(test_label, y_pred)\n",
    "    fpr, tpr, _ = metrics.roc_curve(test_label,  y_pred)\n",
    "    auc = metrics.roc_auc_score(test_label, y_pred)\n",
    "    plt.style.use(\"fivethirtyeight\")\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(fpr, tpr, label=\"{}, auc=\".format(model)+str(auc), color='green', linewidth=2)\n",
    "    ax.set_title(\"ROC curve\", fontdict=font)\n",
    "    leg = ax.legend(loc=\"best\")\n",
    "    text = leg.get_texts()\n",
    "    _ = plt.setp(text, color=\"blue\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_score(y_pred, test_label):\n",
    "    return (((y_pred == test_label).sum(axis=0)\n",
    "            - (y_pred - test_label) @ y_pred) \n",
    "            / y_pred.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBClassifier()               \n",
    "model.fit(train_data, train_label)            \n",
    "y_pred = model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(test_label, y_pred, \"xgboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     class_0       0.99      0.96      0.98      4073\n",
      "     class_1       0.93      0.98      0.95      1923\n",
      "\n",
      "   micro avg       0.97      0.97      0.97      5996\n",
      "   macro avg       0.96      0.97      0.97      5996\n",
      "weighted avg       0.97      0.97      0.97      5996\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "target_names = [\"class_0\", \"class_1\"]\n",
    "print(classification_report(y_pred, test_label, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(\"test.csv\")\n",
    "id_ = test[\"id\"]\n",
    "test_features = test.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame()\n",
    "result[\"id\"] = id_\n",
    "result[\"safe_type\"] = predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv(\"result.csv\", encoding=\"utf-8\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9566377585056705"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_score(y_pred, test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
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